In a centralized control scheme, the control of devices is performed centrally: all information and knowledge is present in a central point where all decisions are taken and according commands are sent to the devices/customers.
The main advantage of a central control is that the control can be optimal in the strict sense, all information (including the electricity grid information) required is used at the full extent to allow for the mathematically optimal solution. Diagnostics and error handling can be done in one centralized architecture where all information can be balanced and processed.
However a main disadvantage is the need for information, to allow for a mathematically optimal solution, all relevant information has to be present, this includes the start and stop times of the applications, demand patterns, power profiles and electricity grid topology. Such an information hungry approach necessitates the presence of a robust and extended (meaning expensive) communication network. Consumers participating in this approach have to communicate behaviour patterns (start and stop times) and device profiles, in addition this combination can lead to privacy concerns. Another important disadvantage is the complexity of the calculations, the computational complexity can become insurmountable especially in the case where many devices are present, the calculation requirements sealing exponentially with the number of devices at hand.
To overcome these disadvantages a market-based demand side management system is more preferable. For instance the system disclosed in WO 2011/074950 is a market-based demand side management system as known in the art. The power/energy a device wants to consume or can produce is translated into a bidding function. By combining the bidding functions of all devices taking part in the demand response, a demand-supply balance is found. All devices consume/produce the power that, according to their bidding function, corresponds with the market balance priority. WO'950 provides a robust, simple, generic mechanism where privacy is guaranteed, as the devices only communicate their bidding function.
However, high penetration of wind or solar power challenges the future grid operation. Proper electric system operation requires a way to handle the effects of the variability and randomness of wind or solar power and power of other intermittent sources. When transferring the philosophy of demand side management (DSM) for wind power balancing, one preferably has to match the consumer demand with the power generation, rather than to use expensive reserves of flexible generators. Different electric appliances commonly found in a household can shift their consumption over different time slots. Examples of these flexible devices are refrigerators, air conditioners, dish washers, electric boilers and electric vehicles (EVs). In case of a high excess of wind energy most flexible devices will preferably consume power. This might overload the low voltage network distribution transformer or making it difficult to comply with national standards to keep the voltage within acceptable limits. Simultaneous charging of electric vehicle can create undervoltage problems in low voltage networks. Therefore measures needs to be taken to avoid voltage problems. DSM can also be applied to avoid transformer overloading or voltage profile control in distribution systems. Studies have shown that load response is an effective measure to solve power system constraints in a distribution system with high wind power penetrations. As DSM will involve millions of customers, centralized control will be not manageable as limits of computational complexity and communication overhead will be reached. Different authors therefore propose multi-agent systems to obtain a scalable system. A multi-agent system can be applied to reduce imbalance costs with EVs. A multi-agent based Virtual Power Plant consisting of domestic devices can be created to compensate imbalance caused by wind energy. Reducing peak demand can be obtained with a decentralized control.
Therefore, a need exists for improved methods and systems for distributing and/or controlling an energy flow in an electricity network.